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Upper Bound of Bayesian Generalization Error in Partial Concept Bottleneck Model (CBM): Partial CBM outperforms naive CBM
March 15, 2024, 4:42 a.m. | Naoki Hayashi, Yoshihide Sawada
cs.LG updates on arXiv.org arxiv.org
Abstract: Concept Bottleneck Model (CBM) is a methods for explaining neural networks. In CBM, concepts which correspond to reasons of outputs are inserted in the last intermediate layer as observed values. It is expected that we can interpret the relationship between the output and concept similar to linear regression. However, this interpretation requires observing all concepts and decreases the generalization performance of neural networks. Partial CBM (PCBM), which uses partially observed concepts, has been devised to …
abstract arxiv bayesian concept concepts cs.ai cs.lg error intermediate layer math.st networks neural networks stat.ml stat.th type values
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